arXiv 2605.295832026-05-28cs.CV

BitC-3DGS: 基于位压缩的高容量3D高斯泼溅水印技术

BitC-3DGS: High-Capacity 3D Gaussian Splatting Watermarking via Bit Compression

School of Cyber Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李科金医学院）；College of Electronic Engineer, National University of Defense and Technology（国防科技大学电子工程学院）；Institute of AI for Industries, Chinese Academy of Sciences（中国科学院人工智能产业研究所）；School of Computer Science and Engineering, Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University（东南大学计算机科学与工程学院，教育部新一代人工智能技术及其交叉应用重点实验室）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；Faculty of Science and Technology, UOW College Hong Kong（UOW学院香港科技学院理学院）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程学院）

Yuquan Bi, Baosheng Yu, Yingke Lei, Jianwei Yang, Hongsong Wang, Jie Gui, Yuan Yan Tang, James Tin-Yau Kwok

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AI总结：

提出BitC-3DGS框架，通过位压缩令牌化、双分支架构和硬消息采样策略，突破CLIP文本编码器77位消息限制，实现高容量3DGS水印嵌入与恢复。

摘要：

高容量水印对于3D高斯泼溅（3DGS）资产嵌入丰富信息（例如所有权、来源和认证码）是必要的，从而在大规模3D资产管线中实现可靠的识别和完整性验证。现有的基于预训练文本编码器的位到令牌水印方法由于CLIP固定的77令牌上下文长度而仅限于77位消息，因为超出此限制的令牌不被学习的位置嵌入支持。为了解决这一限制，我们引入了BitC-3DGS，一种位压缩框架，每个令牌编码多个消息位。它采用位压缩令牌化方案，将同一块内的多个位编码为单个语义令牌。为了恢复压缩信息，它进一步引入了双分支架构用于联合块解压缩和位解码，以及硬消息采样策略以改善解码器训练期间的组合覆盖。在Blender和LLFF数据集上的大量实验证明了BitC-3DGS在高容量水印方面的有效性，实现了高消息恢复精度和渲染保真度。例如，它支持128位消息容量，恢复精度与最近最先进方法中64位消息相当。

英文摘要：

High-capacity watermarking is necessary for 3D Gaussian Splatting (3DGS) assets to embed rich information (e.g., ownership, provenance, and authentication codes), enabling reliable identification and integrity verification in large-scale 3D asset pipelines. Existing bit-to-token watermarking methods based on a pre-trained text encoder are limited to 77-bit messages due to CLIP's fixed 77-token context length, as tokens beyond this limit are unsupported by learned positional embeddings. To address this limitation, we introduce BitC-3DGS, a bit-compression framework that encodes multiple message bits per token. It employs a bit-compressed tokenization scheme that encodes multiple bits within the same chunk into a single semantic token. To enable recovery of the compressed information, it further introduces a dual-branch architecture for joint chunk decompression and bit decoding, along with a hard-message sampling strategy to improve combinatorial coverage during decoder training. Extensive experiments on the Blender and LLFF datasets demonstrate the effectiveness of BitC-3DGS for high-capacity watermarking, achieving high message recovery accuracy and rendering fidelity. For example, it supports 128-bit message capacity with recovery accuracy comparable to that of 64-bit messages in recent state-of-the-art methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。